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32eca7197a
* update * apply suggestion * fix tests for main branch * remove unused logger * add special tokens in tests * nit * fix more tests * fix test * pg also
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch chameleon model."""
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import tempfile
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import unittest
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from transformers import ChameleonProcessor, LlamaTokenizer
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from transformers.testing_utils import get_tests_dir
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from transformers.utils import is_vision_available
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from ...test_processing_common import ProcessorTesterMixin
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if is_vision_available():
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from transformers import ChameleonImageProcessor
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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class ChameleonProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = ChameleonProcessor
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@classmethod
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def setUpClass(cls):
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cls.tmpdirname = tempfile.mkdtemp()
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image_processor = ChameleonImageProcessor()
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tokenizer = LlamaTokenizer(vocab_file=SAMPLE_VOCAB)
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tokenizer.pad_token_id = 0
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tokenizer.sep_token_id = 1
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tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
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processor = cls.processor_class(image_processor=image_processor, tokenizer=tokenizer, image_seq_length=2)
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processor.save_pretrained(cls.tmpdirname)
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cls.image_token = processor.image_token
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def test_special_mm_token_truncation(self):
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"""Tests that special vision tokens do not get truncated when `truncation=True` is set."""
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processor = self.get_processor()
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input_str = self.prepare_text_inputs(batch_size=2, modality="image")
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image_input = self.prepare_image_inputs(batch_size=2)
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_ = processor(
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text=input_str,
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images=image_input,
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return_tensors="pt",
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truncation=None,
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padding=True,
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)
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with self.assertRaises(ValueError):
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_ = processor(
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text=input_str,
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images=image_input,
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return_tensors="pt",
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truncation=True,
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padding=True,
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max_length=20,
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)
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@staticmethod
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def prepare_processor_dict():
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return {"image_seq_length": 2} # fmt: skip
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